Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non transitory storage medium for execution by, or to control the operation of, data processing apparatus. In particular, the encoder neural network 110 processes the audio data to generate the encoder output 112. While Hard EM achieves impressive performance when reembedding from scratch and when training on only 200 or 500 examples, we In particular, the system generates a decoder input that includes, for each latent variable, the content latent embedding vector identified in the discrete latent representation for the latent variable. 2019. The scores are averages over five random subsamples, with standard deviations in parentheses and column bests in. Our experiments also suggest that Hard EM performs particularly well in case(1) when there is little supervised data, and that VQ-VAE struggles in this setting. For the latent space size, we choose M in {1,2,4,8,16} and K in {128,256,512,1024,4096}. In the local model, when M>1, we concatenate the M embeddings to form a single real vector embedding for the lth latent variable. The system receives input audio data (step 202). We consider two generative models p(x|z;), one where L=T and one where L=1. A1, 32 5 ABSTRACT Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating discrete latent representations of input audio data. <> In VAEs, for example, the bottleneck is generally constructed between the encoder and decoder network blocks. The pitch reconstruction neural network 192 is a neural network that is configured to generate a training reconstruction of a pitch track of received audio inputs conditioned on the same decoder input as the decoder 170. In Figure4 we additionally experiment with a slightly different setting: Rather than retrieving a fixed number of nearest neighbors for a query document, we retrieve all the documents within a neighborhood of Hamming distance D, and calculate the average label precision. is then viewed as the source side input for a standard 1-layer Transformer encoder-decoder model(Vaswani etal., 2017), which decodes x using causal masking. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. However, stochastic neural networks rarely use categorical latent variables due to the inability to. The system obtains the discrete latent representation of the audio data (step 302). Thus, for example, the index database can include multiple collections of data, each of which may be organized and accessed differently. portion of the larger utterance. The system of any one of claims 1 -3, wherein the instructions further cause the one or more computers to implement: a decoder neural network, wherein the decoder neural network is configured to: receive a decoder input derived from the discrete latent representation of the input audio data, and. We motivate our use of discrete latent space through the multi-modal posterior . IB, the encoder output 112 is a sequence of D dimensional vectors, with each position in the sequence corresponding to a respective latent variable. The system of claim 4 or claim 5, wherein the reconstruction of the audio input data is a predicted companded and quantized representation of the audio input data. The system 150 processes the decoder input 162 using the decoder neural network 170 to generate the reconstruction 172 of the input audio data 102, i.e., an estimate of the input based on the latent representation 122. Part 2 covers a latent variable model with continuous latent variables for modeling more complex data, like natural images for example, and a Bayesian inference technique . That is, the set of latent variables only need to be sent from the encoder system 100 to the decoder system 150 once in order for the decoder system 150 to be able to reconstruct audio data. The subsystem may generating the discrete latent representation by, for each of the latent variables in the sequence of latent variables, determining, from the set of content latent embedding vectors in the memory, a content latent embedding vector that is nearest to the encoded vector for the latent variable. and Max Welling. Here we evaluate our global discrete representations in a document retrieval task to directly assess their quality; we note that this evaluation does not rely on the learned code books, embeddings, or a classifier. Test accuracy results by dataset and by the number of labeled examples used in training. Left: K-Means selects a discretized latent variable based on the Euclidean distance to the dense representation z e in the vector space. Also, a computer can interact with a user by 2010. We find CBOW (with 64-dimensional embeddings) to be the most robust in settings with small numbers of labeled instances, Thus, the commitment loss is a constant multiplied by a square of an 12 error between the training encoded vector for the latent variable and the stopgradient of the nearest current latent embedding vector for the latent variable. reparameterization with Gumbel-Softmax. We use accuracy as the evaluation metric. The discrete latent representation can identify a nearest content latent embedding vector in any of a variety of ways. To circumvent this, we modify the KL term to be max(KL,). These works show that we can generate realistic speech and image samples from discrete encodings, which better align with symbolic representations that humans seem to work with (e.g., we naturally encode continuous speech signals into discrete words). In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Generating the discrete latent representation may also comprise generating a speaker vector from at least the encoded vectors in the encoder output, and determining, from the set of speaker latent embedding vectors in the memory, a speaker latent embedding vector that is nearest to the speaker vector. from a continuous space, The mathematics of probably also high dimensional, dense, and continuous. Maruan Al-Shedivat and Ankur Parikh. and at training time step t, the system can set the value as follows: By updating the current content latent embedding vectors in this manner, the system moves at least some of the embedding vectors in the memory towards the encoded vectors in the encoder output. /pdfrw_0 Do In implementations in which the system uses the speaker codebook 134, the encoder subsystem 120 generates, from the encoder output 112, a speaker vector from at least the encoded vectors in the encoder output and determines, from the set of speaker latent embedding vectors in the memory, a speaker latent embedding vector that is nearest to the speaker vector. As is common in VAE-style models of text, we model the text autoregressively, and allow arbitrary interdependence between the text and the latents. The method of any one of claims 9 or 10, wherein updating the current content latent embedding vectors and the current speaker latent embedding vectors comprises: determining an update to the nearest current speaker latent embedding vector for the latent variable by determining a gradient with respect to the nearest current speaker latent embedding vector to minimize an error between the training speaker vector and the nearest current speaker latent embedding vector. endobj In some other implementations, the decoder neural network 170 is an auto regressive neural network, e.g., a WaveNet or other auto-regressive convolutional neural network. Structured Latent Variables, Consistency by 20701180, Country of ref document: VQ-VAE and VQ-VAE2, which uses a heirachy of vector quanisations, now significantly outperform standard VAE architectures across an array of downstream generative tasks. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. Given a vocabulary of size 30,000, storing a T-length sentence requires Tlog23000014.9T bits. <> and posterior collapse in variational autoencoders. When reembedding, local representations tend to improve as we move from M=1 to M=2, but not significantly after that. The subsystem 120 then includes, in the discrete latent representation 122, data that identifies, for each latent variable, the nearest content latent embedding vector to the encoded vector for the latent variable and, when used, the nearest speaker latent embedding vector to the speaker vector. Thus in one aspect there is described a system comprising a memory for storing a set of content latent embedding vectors, and optionally a set of speaker latent embedding vectors. In our setting, a code book is shared across latent vectors. Thus, only the discrete latent representation needs to be transmitted from an encoder system to the decoder system in order for the decoder system to be able to effectively to decode, i.e., reconstruct, the input audio data. This allows the discrete latent representation to be stored using very little storage space and transmitted from the encoder to the decoder using very little network bandwidth, i.e., because both the encoder and decoder have access to the same codebook. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. convolutional neural network that is configured to auto-regressively generate the reconstruction conditioned on the decoder input. Furthermore, because Hard EM requires no sampling, it is a compelling alternative to CatVAE. The encoder system 100 receives input audio data 102 and encodes the input audio data 102 to generate a discrete latent representation 122 of the input audio data 102. For example, the subsystem 120 can determine the content latent embedding vector that is nearest to a given encoded vector using a nearest neighbor lookup on the set of latent embedding vectors or any other appropriate distance metric. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Including the pitch reconstruction neural network 192 in the joint training can improve the quality of reconstructions generated by the decoder 170 after training, without increasing the size of the discrete latent. Concretely, we compare different discrete latent variable models in following steps: Pretraining an encoder-decoder model on in-domain unlabeled text with an ELBO objective, with early stopping based on validation perplexity. We see that using the encoder embeddings typically outperforms reembedding from scratch, and that global representations tend to outperform local ones, except in the full data regime. in case(2), we outperform them. For context representation modeling, we When the example of FIG. Natural Language Processing (EMNLP-IJCNLP). 1A shows an example encoder system 100 and an example decoder system 150. Toyota Technological Institute at Chicago, Truncated Inference for Latent Variable Optimization Problems: Table 1. In the case of the mean-field Categorical VAE, we obtain a length-L sequence of vectors zl{1,,K}M after sampling from the approximate posteriors. Lagging inference networks further assume p(z) to be a fully factorized, uniform prior: p(z)=1KML. This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-18-1-0166. Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating discrete latent representations of input data items. endobj Unshaded and shaded markers correspond to reembedding from scratch and using encoder embeddings, respectively. To clearly understand this let's look at a simple example. For example in some implementations generating the speaker vector comprises applying mean pooling over the encoder vectors. IB, for the latent variable corresponding to a first position in the sequence, the representation 122 identifies the content latent embedding vector e>, while for the latent variable. Only the discrete latent representation needs to be transmitted from an encoder system to a decoder system in order for the decoder system to be able to effectively to decode, i.e., reconstruct, the input audio data. In these implementations, the system generates the speaker vector only from the encoder 12. The convolutional neural network may have a dilated convolutional architecture e.g. In particular, for each latent variable, the system selects the content latent embedding vector stored in the latent embedding vector memory that is nearest to the encoded vector for the latent variable (step 206). The method may further comprise selecting, for each latent variable and from a plurality of current content latent embedding vectors currently stored in the memory, a current latent embedding vector that is nearest to the training encoded vector for the latent variable. In some cases, one or more computers will be dedicated to a particular engine; in other cases, multiple engines can be installed and running on the same computer or computers. A computer program, which may also be referred to or described as a program, software, a software application, an app, a module, a software module, a script, or code, can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages; and it can be deployed in any form, including as a stand alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. In some implementations, during this training, the system also trains a pitch reconstruction neural network 192 jointly with the encoder 110 and the decoder 170. The system of any preceding claim, wherein generating the speaker vector comprises: applying mean pooling over the encoder vectors. (vanden Oord etal., 2017; Razavi etal., 2019). Unsupervised Speech Representation Learning Using Wavenet Autoencoders Jan Chorowski, Ron J; De Novo Molecular Design by Combining Deep Autoencoder; Accent Transfer with Discrete Representation Learning and Latent Space Disentanglement; Autoencoder-Based Initialization for Recur- Rent Neural Networks with a Linear Memory either averaged, or fed to a Transformer and then averaged, and As mentioned, the latent spaces in deep generative models are often sampled to produce new data samples. First, a network maps from the image space, containing real and fake images, to a continuous latent space. The system generates the reconstruction of the input audio data by processing the decoder input using the decoder neural network (step 306). This specification relates to speech coding using neural networks. In some other implementations, the system can update the current content latent embedding vectors as a function of the moving averages of the encoded vectors in the training encoder outputs. For example, for a given embedding vector e,. In case(1), low-dimensional manifolds, indicating that discrete latent variables can learn to represent continuous latent quantities. Both the discrete latent space and its uncertainty estimation are jointly learned during training. 18. stream For convenience, the process 400 will be described as being performed by a system of one or more computers located in one or more locations. Eric Jang, Shixiang Gu, and Ben Poole. As above, for Hard EM, we do not obtain a sequence of discrete vectors from the encoder, but rather a sequence of softmax distributions. 4 is a flow diagram of an example process 400 for training the encoder neural network, the decoder neural network, and updating the latent embedding vectors. Only the discrete latent representation needs to be transmitted from an encoder system to a decoder system in order for the decoder system to be able to effectively to decode, i.e., reconstruct, the input audio data. We see that CatVAE and Hard EM outperform these CBOW baselines (while being significantly more space efficient), while VQ-VAE does not. Time-series Classification without Labels, Face Mask Detection Using ResNet Single Shot Detector With Tensorflow and Keras, Driver Supply Distribution Forecasting for Higher Fulfillment Rate using Machine Learning, Effect of the size of the embedding space. The discrete latent representation may include, for each of the latent variables (in the sequence), an identifier of the nearest latent embedding vector to the encoded vector for the latent variable and optionally an identifier of the speaker latent embedding vector that is nearest to the speaker vector. The system 100 then generates the discrete latent representation 122 using the encoder output 112 and the latent embedding vectors stored in the memory 130. A standard Categorical VAE Sepp Hochreiter and Jrgen Schmidhuber. in part due to the simplicity of performing inference over (certain) continuous latents using variational autoencoders and the reparameterization trick. 2. As shown in the schematic, the descrimintor is now formed of three stages. The system generates a decoder input from the discrete latent representation using the latent embedding vectors (step 304). logic flows can also be performed by special purpose logic circuitry, e.g., an FPGA or an ASIC, or by a combination of special purpose logic circuitry and one or more. Discrete latent spaces compress the information bottleneck and enforce a regularisation upon the latent space. Discrete Latent Variable Representations for Low-Resource Text Classification Shuning Jin , Sam Wiseman , Karl Stratos , Karen Livescu Abstract While much work on deep latent variable models of text uses continuous latent variables, discrete latent variables are interesting because they are more interpretable and typically more space efficient. endobj 8 0 obj Using the VQ method allows the model to circumvent issues of "posterior collapse" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. variables, discrete latent variables are interesting because they are more By generating the speaker vector using global information, i.e., information from at least an entire portion of the input audio, that does not vary over time, the encoder neural network can use the time-varying set of codes, i.e., the individual content embedding vectors, to encode the message content which varies over time, while summarizing and passing speaker-related information through the separate non time- varying set of codes, i.e., the speaker latent embedding vectors. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a. communication network. In other implementations, the subsystem 120 is performing online audio coding and the current input audio is a portion of a larger utterance, i.e., the most recently received portion of the larger utterance. We consider several approaches to learning discrete latent variable models for text in the case where exact marginalization over these variables is intractable. In these implementations, the encoder system 120 stores the discrete latent representation 122 (or a further compressed version of the discrete latent representation 122) in a local memory accessible by the one or more computers so that the discrete latent representation (or the further compressed version of the discrete latent representation) can be accessed by the decoder system 150. The method of any one of claims 9-12, wherein determining the gradient with respect to the current values of the encoder network parameters comprises: copying gradients from the decoder input to the encoder output without updating the current speaker latent embedding vectors or current content latent embedding vectors. Generally, an auto-regressive neural network is a neural network that generates outputs in an auto-regressive manner, i.e., generates the current output conditioned on the outputs that have already been generated. For example, the system can apply mean pooling over the time dimension, i.e., over the encoded vectors in the encoder output, to generate the speaker vector. Data processing apparatus for implementing machine learning models can also include, for example, special-purpose hardware accelerator units for processing common and compute-intensive parts of machine learning training or production, i.e., inference, workloads. 2017. Most of this work has modeled the latent variables as being continuous, that is, as vectors in, , That is, we take the mth ~d-length subvector of ht. This specification uses the termconfigured in connection with systems and computer program components. 2016. The latent space is ideally a minimal representation of the semantic and spatial information found in the data distribution. M any recent advances in the accomplishments of deep generative models have stemmed from a simple yet powerful concept. Computers suitable for the execution of a computer program can be based on general or special purpose microprocessors or both, or any other kind of central processing unit. finally fed into a linear layer followed by a softmax. classifiers with discriminative cluster embeddings, A multi-task Variational auto-encoders were developed to enforce regularisation on the learned latent representation. (2018). SamuelR. Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and generate a discrete latent representation of the input audio data from the encoder output, comprising: for each of the latent variables in the sequence of latent variables, determining, from the set of content latent embedding vectors in the memory, a content latent embedding vector that is nearest to the encoded vector for the latent variable; generating a speaker vector from at least the encoded vectors in the encoder output; and. The decoder neural network 170 has been trained to process the decoder input 162 to generate the reconstruction 172 of the input audio data 102 in accordance with a set of parameters (referred to in this specification asdecoder network parameters). The decoder system 150 includes a decoder subsystem 160 and a decoder neural network 170. Generating a discrete latent representation is described in more detail below with reference to FIGS. The posterior categorical distribution q(zjx) probabilities are dened as one-hot as follows: q(z= kjx) = 1 for k = argmin jkz e(x) e jk 2, 0 . Generally, the discrete latent representation identifies a respective value for each latent variable in a sequence; the sequence may have a fixed number of latent variables. We see that on all datasets when there are only 200 or 500 labeled examples, our best model outperforms VAMPIRE and the CBOW baseline, and our models that reembed the latents from scratch match or outperform VAMPIRE. 2 0 obj <> Introduced by Oord et al. 23. 11 0 obj Many recent advances in the accomplishments of deep generative models have stemmed from a simple yet powerful concept. 2018. The two main motivations are (i)discrete variables are potentially better fit to capture the structure of data such as text and (ii)to prevent the posterior collapse in VAEs that leads to latent variables being ignored when the decoder is too powerful. Neural discrete representation learning. The latent space has a particularly profound effect during inference, where both GANs and VAEs are used to generate images by sampling the latent space, then running these samples through the generator and decoder blocks respectively. we distinguish between local and global models, and between when the discrete representations are reembedded from scratch and when the encoder embeddings are used. regularity crossword clue 7 letters; cisco catalyst 3650 48 port The system 100 then generates the latent representation 122 that identifies, for each of the latent variables, the nearest content latent embedding vector to the encoded vector for the latent variable. Examples of communication networks include a local area network (LAN) and a wide area network (WAN), e.g., the Internet. Like reference numbers and designations in the various drawings indicate like elements. The accuracies obtained by Hard EM, Categorical VAE, and VQ-VAE representations, averaged over the AG News, DBPedia, and Yelp Full development datasets, for different numbers of labeled training examples. The encoder neural network can have any appropriate architecture that allows the neural network to map audio data to a sequence of vectors. Perhaps most interestingly, we note that when reembedding from scratch, Hard EM significantly outperforms the other approaches in the lowest data regimes (i.e., for 200 and 500 examples). In the example of FIG. Despite its success in speech and vision, VQ-VAE has not been considered as much in NLP. The system determines updates to the current latent embedding vectors that are stored in the memory (step 408). In the local model, we obtain token-level embedding vectors by concatenating the M subvectors corresponding to each word. variational autoencoders for text modeling using dilated convolutions, Character-level convolutional networks for text classification, Unsupervised discrete The encoder system 100 and decoder system 150 are examples of systems implemented as computer programs on one or more computers in one or more locations, in which the systems, components, and techniques described below can be implemented. The resulting codebook has parallels in contemporary natural language models where the contextualised word embeddings are represented by a vector. 5 0 obj , wonder whether this performance is due to the alternating optimization, to the multiple E-step updates per M-step update, or to the lack of sampling. In Advances in Neural Information Processing Systems, 2017. by controlling growth of a volume of the embedding space. In implementations the encoder neural network is convolutional neural network. In this specification, the termdatabase is used broadly to refer to any collection of data: the data does not need to be structured in any particular way, or structured at all, and it can be stored on storage devices in one or more locations. Gradients of the likelihood term with respect to enc(x) are again estimated with the straight-through gradient estimator. We on the training data. Alternatively or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or. Both the discrete latent space and its uncertainty estimation are jointly learned during training. and learn a classifier on the discrete representations from scratch. As in VAEs, the encoder and decoder networks are trained simultaneously, with the addition of the discrete codebook. The method may further comprise updating the current content latent embedding vectors and, where used, the current speaker latent embedding vectors.
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